A shortcut to cancer prevention

To better understand and perhaps prevent cancers caused by multiple genetic mutations, Rice University researchers are building a theoretical framework.

New theory suggests mutations have few simple ways to establish themselves in cells and cause tumors

For many researchers, the road to cancer prevention is long and difficult, but a recent study by scientists at Rice University suggests there may be shortcuts.

A theoretical framework is being developed by Rice scientist Anatoly Kolomeisky, postdoctoral researcher Hamid Teimouri and research assistant Cade Spaulding that will explain how cancers caused by multiple genetic mutations could be more easily recognized and possibly prevented. .

Cade Spaulding, Anatoly Kolomeisky and Hamid Teimouri

A new paper from a lab at Rice University shows how to increase the chances of identifying cancer-causing mutations before tumors take hold. The authors are, from left, Cade Spaulding, Anatoly Kolomeisky and Hamid Teimouri. Credit: Rice University

It does this by detecting and ignoring transitional pathways that do not contribute significantly to fixing mutations in a cell that later becomes a tumor.

The study, which was published on May 13, 2022 in the Biophysical Review, details their analysis of the effective energy landscapes of cellular transformation pathways linked to a number of cancers. The ability to reduce the number of pathways to those most likely to initiate cancer could aid in the development of strategies to interrupt the process before it begins.

“In a sense, cancer is a bad luck story,” said Kolomeisky, a professor of chemistry and chemical and biomolecular engineering. “We think we can reduce the likelihood of this bad luck by looking for collections of low-probability mutations that typically lead to cancer. Depending on the type of cancer, this can vary between two mutations and 10.”

The calculation of the effective energies which govern the interactions in the biomolecular systems can help to anticipate their behavior. The theory is widely used to predict how a protein will fold based on the sequence of its constituent atoms and how they interact.

The Rice team applies the same idea to cancer initiation pathways that operate in cells but sometimes include mutations that go undetected by the body’s protections. When two or more of these mutations are fixed in a cell, they continue when cells divide and tumors develop.

Rice University algorithm

An algorithm developed at Rice University identifies and ignores transitional pathways that don’t contribute much to fixing mutations in a cell that goes on to establish a tumor. Credit: Hamid Teimouri/Rice University

According to their calculations, the odds favor the most dominant pathways, those that transport mutations while expending the least energy, Kolomeisky said.

“Instead of looking at all possible chemical reactions, we identify the few that we might need to look at,” he explained. “It seems to us that most of the tissues involved in the initiation of cancer try to be as homogeneous as possible. The rule is that a pathway that decreases heterogeneity will always be the fastest on the pathway to tumor formation.

The large number of possible pathways seems to make their reduction an insoluble problem. “But it turned out that using our chemical intuition and building an effective free-energy landscape helped us by allowing us to calculate where in the process a mutation is likely to attach itself in a cell,” Kolomeisky said.

The team simplified the calculations by initially focusing on pathways involving just two mutations that, once fixed, initiate a tumor. Kolomeisky said the mechanisms involving more mutations would complicate the calculations, but the procedure remains the same.

Much of the credit goes to Spaulding, who, under Teimouri’s guidance, created the algorithms that greatly simplify the calculations. The visiting research assistant was 12 when he first met Kolomeisky for advice. A graduate of a Houston high school two years earlier, he joined the Rice Lab last year at age 16 and will attend Trinity University in San Antonio this fall.

“Cade has exceptional ability in computer programming and in implementing sophisticated algorithms despite his very young age,” Kolomeisky said. “He offered the most efficient Monte Carlo simulations to test our theory, where the system size can involve up to a billion cells.”

Spaulding said the project brought together chemistry, physics and biology in a way that suited his interests, as well as his computer programming skills. “It was a good way to combine all branches of science and also programming, which I find most interesting,” he said.

The study follows a 2019 paper in which the Rice lab modeled stochastic (random) processes to understand why some cancer cells overcome the body’s defenses and trigger the spread of disease.

But understanding how these cells become cancerous in the first place could help avoid them on the cervix, Kolomeisky said. “This has implications for personalized medicine,” he said. “If a tissue test can detect mutations, our framework can tell you if you are likely to develop a tumor and if you need more frequent checkups. I think this powerful framework can be a prevention tool.

The Welch Foundation (C-1559), the National Science Foundation (1953453, 1941106), and the NSF-supported Center for Theoretical Biological Physics (2019745) supported the research.

Reference: “Optimal paths control fixation of multiple mutations during cancer initiation” by Hamid Teimouri, Cade Spaulding and Anatoly B. Kolomeisky, May 13, 2022, Biophysical Journal.
DOI: 10.1016/j.bpj.2022.05.011


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